Eigenvector-based centralities for multilayer temporal networks under the framework of tensor computation

Lv, Laishui; Zhang, Kun; Zhang, Ting; Li, Xun; Sun, Qi; Zhang, Lilinqing; Xue, Wei

doi:10.1016/j.eswa.2021.115471

Cited by 14 publications

(4 citation statements)

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“…Betweenness and closeness centrality both consider influential agents and depend on their capabilities of controlling information flow in the global network, whereas the eigenvector-like centralities (i.e., eigenvector centrality and PageRank) calculate the importance of an agent based on both the quantity and quality of its neighbors. With a distinctive emphasis on the network structure, these centralities may be regarded as generalizations of the centralities of static networks ( 71 ), with potential uses in a wide range of applications. For instance, it has been proposed that betweenness centrality is appropriate to prevent the spread of negative health behavior in a network-based intervention ( 72 ).…”

Section: Discussionmentioning

confidence: 99%

“…However, the promotion of positive health behavior based on the closeness centrality method might be an efficient strategy ( 67 ), as the energetic message or opinion (e.g., PA) would reach the entire network easily without any subgroups being excluded from the intervention ( 73 ). Significantly, eigenvector-like centralities that take immediate, mediative, and global effects of social interactions into account ( 74 ) have been shown to be successful and effective at assigning centrality weights to the nodes in a network to determine the influence of social peers ( 71 ).…”

Section: Discussionmentioning

confidence: 99%

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The Influence of a School Social Network Intervention on Adolescent's Health Behaviors: A Gender-Specific Agent-Based Model

Shu

Xiao

2022

Front. Public Health

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IntroductionAdolescence is a crucial stage for health behavior development, which is associated with health in adulthood. School closures caused by the coronavirus disease 2019 (COVID-19) pandemic have exposed adolescents to an increased risk of obesity due to a lack of physical activity. Although social network interventions provide an effective approach for promoting health-related behavior, current practices neglect gender differences in adolescent behavioral patterns and emotional preferences. The aim of this study was to examine the effectiveness of centrality-based methods integrated with of gender contexts in a social network intervention to improve adolescent's health behavior.MethodsWe developed an agent-based model (ABM) that supports the small-world characteristics of adolescent social networks. Health-related data for junior middle school students (n = 234, 48% girls) were collected in November 2018, 2019 and 2020 in Tianjin, China. We simulated multiple network-based interventions with different criteria for influential agents (i.e., betweenness centrality, closeness centrality, eigenvector centrality, and PageRank) and a random condition. The rules for generating peer influence and accelerating behavioral changes were based on the diffusion of innovations theory, with gender specifications.ResultsAfter the school closures, there was a significant increase in the prevalence of overweight and obesity among adolescents, with a greater increase in girls than in boys (+8.85% vs. +1.65%, p < 0.001). Simulations showed that centrality-based network interventions were more effective than the random condition (average 6.17% per tick vs. 5.22% per tick, p < 0.05), with a higher efficiency in girls than boys (average 3.68% vs. 2.99% per tick, p < 0.05). PageRank outperformed other centrality conditions at the population level (6.37% per tick, p < 0.05). In girls, betweenness centrality was the best method (3.85% per tick, p < 0.05), while in boys, PageRank still had the greatest efficiency (3.21% per tick, p < 0.05).ConclusionsWe found evidence for gender differences in the negative impact of COVID-19-related school closures and the potential for centrality-based social network interventions to affect adolescent health behavior. Therefore, we emphasize the importance of gender-specific targeting strategies to further promote health-related school programs in the post-pandemic era.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The Influence of a School Social Network Intervention on Adolescent's Health Behaviors: A Gender-Specific Agent-Based Model

Shu

Xiao

2022

Front. Public Health

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“…So far, for the key node identification problem of single-layer networks, a variety of identification methods have been proposed for specific problems. These methods can be classified according to their essential ideas, including eigenvectorsbased method 9 , node removal shrinkage-based method 10 , and graph entropy theory-based method 11 . Considering that identification methods based on a single attribute may ignore other characteristics, a variety of key node identification methods based on multi-attribute fusion have also been proposed 12 .…”

Section: Introductionmentioning

confidence: 99%

Key node identification for a network topology using hierarchical comprehensive importance coefficients

Qiu,

Yu,

Feng

et al. 2024

Preprint

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Key nodes are similar to important hubs in a network structure, which can directly determine the robustness and stability of the network. By effectively identifying and protecting these critical nodes, the robustness of the network can be improved, making it more resistant to external interference and attacks. There are various topology analysis methods for existing networks, but key node identification methods often only focus on local attributes or global attributes. Designing an algorithm that combines both attributes can improve the accuracy of key node identification. In this paper, the constraint coefficient of a weakly connected network is calculated based on the Salton indicator, and the hierarchical tenacity global coefficient is obtained by an improved K-Shell decomposition method. Then, a hierarchical comprehensive node importance identification algorithm is proposed which can comprehensively indicate the local and global attributes of the network nodes. Experimental results on real network datasets show that the proposed algorithm outperforms the other classic algorithms in terms of connectivity, average remaining edges, sensitivity and monotonicity.

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“…So far, for the key node identification problem of single-layer networks, a variety of identification methods have been proposed. These methods can be classified according to their essential ideas, including eigenvectors-based method 9 , node removal shrinkage-based method 10 , and graph entropy theory-based method 11 . Considering that identification methods based on a single attribute may ignore other characteristics, a variety of key node identification methods based on multi-attribute fusion have also been proposed 12 .…”

Section: Introductionmentioning

confidence: 99%

Key node identification for a network topology using hierarchical comprehensive importance coefficients

Qiu,

Yu,

Feng

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Key nodes are similar to important hubs in a network structure, which can directly determine the robustness and stability of the network. By effectively identifying and protecting these critical nodes, the robustness of the network can be improved, making it more resistant to external interference and attacks. There are various topology analysis methods for a given network, but key node identification methods often focus on either local attributes or global attributes. Designing an algorithm that combines both attributes can improve the accuracy of key node identification. In this paper, the constraint coefficient of a weakly connected network is calculated based on the Salton indicator, and a hierarchical tenacity global coefficient is obtained by an improved K-Shell decomposition method. Then, a hierarchical comprehensive key node identification algorithm is proposed which can comprehensively indicate the local and global attributes of the network nodes. Experimental results on real network datasets show that the proposed algorithm outperforms the other classic algorithms in terms of connectivity, average remaining edges, sensitivity and monotonicity.

show abstract

Eigenvector-based centralities for multilayer temporal networks under the framework of tensor computation

Cited by 14 publications

References 61 publications

The Influence of a School Social Network Intervention on Adolescent's Health Behaviors: A Gender-Specific Agent-Based Model

The Influence of a School Social Network Intervention on Adolescent's Health Behaviors: A Gender-Specific Agent-Based Model

Key node identification for a network topology using hierarchical comprehensive importance coefficients

Key node identification for a network topology using hierarchical comprehensive importance coefficients

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